Tooth loss in periodontal patients.
Bibliographic record
Abstract
OBJECTIVE: To compare tooth loss between patients who received surgical therapy for chronic periodontitis and those who received nonsurgical therapy alone. METHODS: A retrospective chart study was conducted at Dalhousie University. All patients who had periodontal treatment and were active cases for at least 10 years were included (n = 335). The sample consisted of 120 males (35.8%) and 215 females (64.2%). Ages ranged from 16 to 77 (mean = 46.1 +/- 12.0 years). All patients received nonsurgical therapy; 44.8% received periodontal surgery as well. Variables recorded were demographics, initial attachment loss, treatment type, recall frequency, patient compliance and history of extracted teeth. Independent t-tests or chi-squared tests were used to compare these for surgical and nonsurgical patients. ANOVA was used to test for interactions between initial attachment loss, age, gender, compliance and type of therapy a patient received as reasons for tooth loss. RESULTS: 521 teeth were lost in 69 patients (20.6% of sample). Of teeth lost, 61.8% were due to periodontal disease; 24.8% to caries; 13.2% to other reasons. Patients initially diagnosed with early attachment loss lost an average of 0.37 (+/- 1.33) teeth. Patients diagnosed with moderate attachment loss lost an average of 1.50 (+/- 2.54) teeth and those diagnosed with advanced attachment loss lost an average of 3.11 (+/- 3.01) teeth. Those who received surgical therapy lost more teeth (mean = 1.31 +/- 2.36) than those who received nonsurgical treatment (mean = 0.68 +/- 1.87; p = 0.001). However, initial attachment loss was the only factor that could predict tooth loss. The type of therapy (surgical or nonsurgical) was not statistically significant. CONCLUSIONS: Most periodontal patients (79.4%) who received treatment at this dental school clinic did not lose any teeth due to periodontal disease over at least 10 years. Although patients who had surgical therapy lost more teeth than those who had nonsurgical therapy alone, this was not an important predictor of tooth loss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".